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   <div id="projectname">Reranker Framework (ReFr)
   
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   <div id="projectbrief">Reranking framework for structure prediction and discriminative language modeling</div>
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<div class="title">model-combine-shards.C</div>  </div>
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<a href="model-combine-shards_8_c.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001 <span class="comment">// Copyright 2012, Google Inc.</span>
<a name="l00002"></a>00002 <span class="comment">// All rights reserved.</span>
<a name="l00003"></a>00003 <span class="comment">// </span>
<a name="l00004"></a>00004 <span class="comment">// Redistribution and use in source and binary forms, with or without</span>
<a name="l00005"></a>00005 <span class="comment">// modification, are permitted provided that the following conditions are</span>
<a name="l00006"></a>00006 <span class="comment">// met:</span>
<a name="l00007"></a>00007 <span class="comment">// </span>
<a name="l00008"></a>00008 <span class="comment">//   * Redistributions of source code must retain the above copyright</span>
<a name="l00009"></a>00009 <span class="comment">//     notice, this list of conditions and the following disclaimer.</span>
<a name="l00010"></a>00010 <span class="comment">//   * Redistributions in binary form must reproduce the above</span>
<a name="l00011"></a>00011 <span class="comment">//     copyright notice, this list of conditions and the following disclaimer</span>
<a name="l00012"></a>00012 <span class="comment">//     in the documentation and/or other materials provided with the</span>
<a name="l00013"></a>00013 <span class="comment">//     distribution.</span>
<a name="l00014"></a>00014 <span class="comment">//   * Neither the name of Google Inc. nor the names of its</span>
<a name="l00015"></a>00015 <span class="comment">//     contributors may be used to endorse or promote products derived from</span>
<a name="l00016"></a>00016 <span class="comment">//     this software without specific prior written permission.</span>
<a name="l00017"></a>00017 <span class="comment">//</span>
<a name="l00018"></a>00018 <span class="comment">// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS</span>
<a name="l00019"></a>00019 <span class="comment">// &quot;AS IS&quot; AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT</span>
<a name="l00020"></a>00020 <span class="comment">// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR</span>
<a name="l00021"></a>00021 <span class="comment">// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT</span>
<a name="l00022"></a>00022 <span class="comment">// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,</span>
<a name="l00023"></a>00023 <span class="comment">// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT</span>
<a name="l00024"></a>00024 <span class="comment">// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,           </span>
<a name="l00025"></a>00025 <span class="comment">// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY           </span>
<a name="l00026"></a>00026 <span class="comment">// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT</span>
<a name="l00027"></a>00027 <span class="comment">// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE</span>
<a name="l00028"></a>00028 <span class="comment">// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</span>
<a name="l00029"></a>00029 <span class="comment">// -----------------------------------------------------------------------------</span>
<a name="l00030"></a>00030 <span class="comment">//</span>
<a name="l00031"></a>00031 <span class="comment">//</span>
<a name="l00035"></a>00035 <span class="comment"></span>
<a name="l00036"></a>00036 <span class="preprocessor">#include &lt;cstdio&gt;</span>
<a name="l00037"></a>00037 <span class="preprocessor">#include &lt;iostream&gt;</span>
<a name="l00038"></a>00038 <span class="preprocessor">#include &lt;string&gt;</span>
<a name="l00039"></a>00039 <span class="preprocessor">#include &lt;tr1/memory&gt;</span>
<a name="l00040"></a>00040 <span class="preprocessor">#include &lt;math.h&gt;</span>
<a name="l00041"></a>00041 <span class="preprocessor">#include &quot;../proto/data.pb.h&quot;</span>
<a name="l00042"></a>00042 <span class="preprocessor">#include &quot;../proto/dataio.h&quot;</span>
<a name="l00043"></a>00043 <span class="preprocessor">#include &quot;../proto/model.pb.h&quot;</span>
<a name="l00044"></a>00044 <span class="preprocessor">#include &quot;../utils/kdebug.h&quot;</span>
<a name="l00045"></a>00045 <span class="preprocessor">#include &quot;<a class="code" href="candidate_8_h.html" title="Provides the reranker::Candidate class for representing a candidate hypothesis from an initial model...">candidate.H</a>&quot;</span>
<a name="l00046"></a>00046 <span class="preprocessor">#include &quot;<a class="code" href="candidate-set_8_h.html" title="Class to hold a single training instance for a reranker, which is a set of examples, typically the n-best output of some input process, posibly including a gold-standard feature vector.">candidate-set.H</a>&quot;</span>
<a name="l00047"></a>00047 <span class="preprocessor">#include &quot;<a class="code" href="candidate-set-iterator_8_h.html" title="Provides an interface and some implementations for iterating over CandidateSet  instances.">candidate-set-iterator.H</a>&quot;</span>
<a name="l00048"></a>00048 <span class="preprocessor">#include &quot;<a class="code" href="candidate-set-reader_8_h.html" title="Class for reading streams of training or test instances, where each training or test instance is a re...">candidate-set-reader.H</a>&quot;</span>
<a name="l00049"></a>00049 <span class="preprocessor">#include &quot;<a class="code" href="factory_8_h.html" title="Provides a generic dynamic object factory.">factory.H</a>&quot;</span>
<a name="l00050"></a>00050 <span class="preprocessor">#include &quot;<a class="code" href="model-merge-reducer_8_h.html" title="Reducer classes for trainer.">model-merge-reducer.H</a>&quot;</span>
<a name="l00051"></a>00051 <span class="preprocessor">#include &quot;<a class="code" href="model-reader_8_h.html" title="Provides the ModelReader  class, which can create Model  instances from a file.">model-reader.H</a>&quot;</span>
<a name="l00052"></a>00052 
<a name="l00053"></a><a class="code" href="model-combine-shards_8_c.html#a6318e7c5c7d2d52e21cdfb06906c8ae2">00053</a> <span class="preprocessor">#define DEFAULT_MODEL_PROTO_READER_SPEC &quot;PerceptronModelProtoReader()&quot;</span>
<a name="l00054"></a>00054 <span class="preprocessor"></span>
<a name="l00055"></a>00055 <span class="keyword">using namespace </span>std;
<a name="l00056"></a>00056 <span class="keyword">using namespace </span>std::tr1;
<a name="l00057"></a>00057 <span class="keyword">using namespace </span>reranker;
<a name="l00058"></a>00058 <span class="keyword">using</span> confusion_learning::FeatureMessage;
<a name="l00059"></a>00059 <span class="keyword">using</span> confusion_learning::ModelMessage;
<a name="l00060"></a>00060 
<a name="l00061"></a><a class="code" href="model-combine-shards_8_c.html#a0ddf1224851353fc92bfbff6f499fa97">00061</a> <span class="keywordtype">int</span> <a class="code" href="candidate-set-proto-reader-test_8_c.html#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span>* argv[]) {
<a name="l00062"></a>00062   <span class="keywordtype">int</span> option_char;
<a name="l00063"></a>00063   <span class="keywordtype">bool</span> use_integer_feats = <span class="keyword">false</span>;
<a name="l00064"></a>00064   <span class="keywordtype">string</span> output_name;
<a name="l00065"></a>00065   <span class="keywordtype">string</span> devtest_filename;
<a name="l00066"></a>00066   <span class="keywordtype">int</span> max_examples_to_read = -1;
<a name="l00067"></a>00067 
<a name="l00068"></a>00068   <span class="comment">// Invokes member function `int operator ()(void);&#39;</span>
<a name="l00069"></a>00069   <span class="keywordflow">while</span> ((option_char = getopt(argc, argv, <span class="stringliteral">&quot;Io:d:n:&quot;</span>)) != EOF) {
<a name="l00070"></a>00070     <span class="keywordflow">switch</span> (option_char) {  
<a name="l00071"></a>00071     <span class="keywordflow">case</span> <span class="charliteral">&#39;I&#39;</span>:
<a name="l00072"></a>00072       use_integer_feats = <span class="keyword">true</span>;
<a name="l00073"></a>00073       <span class="keywordflow">break</span>;
<a name="l00074"></a>00074     <span class="keywordflow">case</span> <span class="charliteral">&#39;o&#39;</span>:
<a name="l00075"></a>00075       output_name = optarg;
<a name="l00076"></a>00076       <span class="keywordflow">break</span>;
<a name="l00077"></a>00077     <span class="keywordflow">case</span> <span class="charliteral">&#39;d&#39;</span>:
<a name="l00078"></a>00078       devtest_filename = optarg;
<a name="l00079"></a>00079       <span class="keywordflow">break</span>;
<a name="l00080"></a>00080     <span class="keywordflow">case</span> <span class="charliteral">&#39;n&#39;</span>:
<a name="l00081"></a>00081       max_examples_to_read = atoi(optarg);
<a name="l00082"></a>00082       <span class="keywordflow">break</span>;
<a name="l00083"></a>00083     <span class="keywordflow">case</span> <span class="charliteral">&#39;?&#39;</span>:
<a name="l00084"></a>00084       cerr &lt;&lt; <span class="stringliteral">&quot;usage: &quot;</span> &lt;&lt; argv[0]
<a name="l00085"></a>00085            &lt;&lt; <span class="stringliteral">&quot; [-E] [-I] [-d &lt;devtest examples&gt;] [-o &lt;output file&gt;]&quot;</span>
<a name="l00086"></a>00086            &lt;&lt; endl;
<a name="l00087"></a>00087       cerr &lt;&lt; <span class="stringliteral">&quot;-E - normalize with the total number of errors&quot;</span> &lt;&lt; endl;
<a name="l00088"></a>00088       cerr &lt;&lt; <span class="stringliteral">&quot;-I - use integer feature id&#39;s from proto&quot;</span> &lt;&lt; endl;
<a name="l00089"></a>00089       <span class="keywordflow">return</span> -1;
<a name="l00090"></a>00090       <span class="keywordflow">break</span>;
<a name="l00091"></a>00091     }
<a name="l00092"></a>00092   }
<a name="l00093"></a>00093 
<a name="l00094"></a>00094   ModelMessage model_with_feats;
<a name="l00095"></a>00095   <span class="comment">// Process each of the input records.  This reducer assumes that the input is</span>
<a name="l00096"></a>00096   <span class="comment">// &lt;FeatureId, encoded FeatureMessage&gt; pair per line in the following format:</span>
<a name="l00097"></a>00097   <span class="comment">// FeatureString | EncodedMsg \n</span>
<a name="l00098"></a>00098   ConfusionProtoIO reader;
<a name="l00099"></a>00099   ConfusionProtoIO* writer;
<a name="l00100"></a>00100   <span class="keywordflow">if</span> (output_name.empty()) {
<a name="l00101"></a>00101     writer = <span class="keyword">new</span> ConfusionProtoIO(<span class="stringliteral">&quot;&quot;</span>, ConfusionProtoIO::WRITESTD, <span class="keyword">false</span>, <span class="keyword">true</span>);
<a name="l00102"></a>00102   } <span class="keywordflow">else</span> {
<a name="l00103"></a>00103     writer = <span class="keyword">new</span> ConfusionProtoIO(output_name, ConfusionProtoIO::WRITE, <span class="keyword">true</span>,
<a name="l00104"></a>00104                                   <span class="keyword">true</span>);
<a name="l00105"></a>00105   }
<a name="l00106"></a>00106   <span class="keywordflow">while</span> (cin) {
<a name="l00107"></a>00107     <span class="comment">// Process input.</span>
<a name="l00108"></a>00108     <span class="keywordtype">string</span> input_data;
<a name="l00109"></a>00109     getline(cin, input_data);
<a name="l00110"></a>00110     <span class="keywordflow">if</span> (input_data.empty()) {
<a name="l00111"></a>00111       <span class="keywordflow">break</span>;
<a name="l00112"></a>00112     }
<a name="l00113"></a>00113     <span class="keywordtype">int</span> delim_pos = input_data.find(<span class="charliteral">&#39;\t&#39;</span>);
<a name="l00114"></a>00114     <span class="keywordtype">string</span> feat_id = input_data.substr(0, delim_pos);
<a name="l00115"></a>00115     <span class="keywordtype">string</span> value = input_data.substr(delim_pos + 1);
<a name="l00116"></a>00116 
<a name="l00117"></a>00117     <span class="keywordflow">if</span> (feat_id.compare(ModelInfoReducer::kModelMessageFeatureName) == 0) {
<a name="l00118"></a>00118       <span class="keywordflow">if</span> (model_with_feats.num_iterations() &gt; 0) {
<a name="l00119"></a>00119         cerr &lt;&lt; <span class="stringliteral">&quot;Merging in more than one model message.&quot;</span> &lt;&lt; endl;
<a name="l00120"></a>00120         <span class="keywordflow">return</span> -1;
<a name="l00121"></a>00121       }
<a name="l00122"></a>00122       ModelMessage new_model;
<a name="l00123"></a>00123       <span class="keywordflow">if</span> (!reader.DecodeBase64(value, &amp;new_model)) {
<a name="l00124"></a>00124         cerr &lt;&lt; <span class="stringliteral">&quot;Error decoding message: &quot;</span> &lt;&lt; value.c_str() &lt;&lt; endl;
<a name="l00125"></a>00125       }
<a name="l00126"></a>00126       <span class="comment">// Output model message</span>
<a name="l00127"></a>00127       model_with_feats.MergeFrom(new_model);
<a name="l00128"></a>00128       writer-&gt;Write(new_model);
<a name="l00129"></a>00129     } <span class="keywordflow">else</span> {
<a name="l00130"></a>00130       FeatureMessage* feat =
<a name="l00131"></a>00131           model_with_feats.mutable_raw_parameters()-&gt;add_feature();
<a name="l00132"></a>00132       <span class="keywordflow">if</span> (!reader.DecodeBase64(value, feat)) {
<a name="l00133"></a>00133         cerr &lt;&lt; <span class="stringliteral">&quot;Error decoding message: &quot;</span> &lt;&lt; value.c_str() &lt;&lt; endl;
<a name="l00134"></a>00134       }
<a name="l00135"></a>00135     }
<a name="l00136"></a>00136   }
<a name="l00137"></a>00137   <span class="keywordflow">if</span> (model_with_feats.raw_parameters().feature_size() == 0) {
<a name="l00138"></a>00138     cerr &lt;&lt; <span class="stringliteral">&quot;Empty model, nothing to output.&quot;</span> &lt;&lt; endl;
<a name="l00139"></a>00139     <span class="keywordflow">return</span> -1;
<a name="l00140"></a>00140   }
<a name="l00141"></a>00141   <span class="comment">// Normalize the feature values.</span>
<a name="l00142"></a>00142   <span class="keywordflow">for</span> (<span class="keywordtype">int</span> fix = 0; fix &lt; model_with_feats.raw_parameters().feature_size();
<a name="l00143"></a>00143        ++fix) {
<a name="l00144"></a>00144     FeatureMessage* feat =
<a name="l00145"></a>00145       model_with_feats.mutable_raw_parameters()-&gt;mutable_feature(fix);
<a name="l00146"></a>00146     <span class="keywordflow">if</span> (!finite(feat-&gt;value()) || !finite(feat-&gt;avg_value())) {
<a name="l00147"></a>00147       cerr &lt;&lt; <span class="stringliteral">&quot;WARNING: feature &quot;</span> &lt;&lt; feat-&gt;name() &lt;&lt; <span class="stringliteral">&quot; (ID:&quot;</span>
<a name="l00148"></a>00148            &lt;&lt; feat-&gt;id() &lt;&lt; <span class="stringliteral">&quot;) has non-finite value.&quot;</span> &lt;&lt; endl;
<a name="l00149"></a>00149     } <span class="keywordflow">else</span> {
<a name="l00150"></a>00150       <span class="keywordflow">if</span> (model_with_feats.training_errors() &gt; 0) {
<a name="l00151"></a>00151         feat-&gt;set_value(feat-&gt;value() / model_with_feats.training_errors());
<a name="l00152"></a>00152         feat-&gt;set_avg_value(feat-&gt;avg_value() / model_with_feats.training_errors());
<a name="l00153"></a>00153         <span class="keywordflow">if</span> (!finite(feat-&gt;value()) || !finite(feat-&gt;avg_value())) {
<a name="l00154"></a>00154           cerr &lt;&lt; <span class="stringliteral">&quot;WARNING: after error normalization, feature &quot;</span>
<a name="l00155"></a>00155                &lt;&lt; feat-&gt;name() &lt;&lt; <span class="stringliteral">&quot; (ID:&quot;</span> &lt;&lt; feat-&gt;id()
<a name="l00156"></a>00156                &lt;&lt; <span class="stringliteral">&quot;) has non-finite value.&quot;</span> &lt;&lt; endl;
<a name="l00157"></a>00157         }
<a name="l00158"></a>00158       }
<a name="l00159"></a>00159     }
<a name="l00160"></a>00160     writer-&gt;Write(*feat);
<a name="l00161"></a>00161   }
<a name="l00162"></a>00162   <span class="keyword">delete</span> writer;
<a name="l00163"></a>00163 
<a name="l00164"></a>00164   <span class="keywordtype">double</span> <a class="code" href="namespacehadoop-run.html#acccc808b1c10830f49ff1b785c460ddd">loss</a> = 0.0;
<a name="l00165"></a>00165   <span class="keywordflow">if</span> (! devtest_filename.empty()) {
<a name="l00166"></a>00166     <span class="comment">// Evaluate model.</span>
<a name="l00167"></a>00167     <a class="code" href="classreranker_1_1_model_reader.html" title="Knows how to create Model instances that have been serialized to a file.">ModelReader</a> model_reader(1);
<a name="l00168"></a>00168     shared_ptr&lt;Model&gt; model = model_reader.<a class="code" href="classreranker_1_1_model_reader.html#a55f682f65a7c93ecbbc8089108f7edf2">Read</a>(model_with_feats);
<a name="l00169"></a>00169     
<a name="l00170"></a>00170     vector&lt;shared_ptr&lt;CandidateSet&gt; &gt; devtest_examples;
<a name="l00171"></a>00171     <a class="code" href="classreranker_1_1_candidate_set_reader.html" title="A class for reading streams of training or test instances, where each training or test instance is a ...">CandidateSetReader</a> csr(max_examples_to_read, -1, 1000);
<a name="l00172"></a>00172     csr.<a class="code" href="classreranker_1_1_candidate_set_reader.html#a46df40e5ad98bdee0bb0a552512349d5" title="Sets the verbosity of this reader (mostly for debugging purposes).">set_verbosity</a>(1);
<a name="l00173"></a>00173     csr.<a class="code" href="classreranker_1_1_candidate_set_reader.html#afa8d2312498eb123c00796dbc2dd087a" title="Reads a stream of CandidateSet instances from the specified file or from standard input...">Read</a>(devtest_filename, <span class="keyword">true</span>, <span class="keyword">true</span>, <span class="keyword">true</span>, devtest_examples);
<a name="l00174"></a>00174     <span class="keyword">typedef</span> <a class="code" href="classreranker_1_1_collection_candidate_set_iterator.html" title="An implementation of the CandidateSetIterator interface that is backed by an arbitrary C++ collection...">CollectionCandidateSetIterator&lt;vector&lt;shared_ptr&lt;CandidateSet&gt;</a> &gt; &gt;
<a name="l00175"></a>00175         CandidateSetVectorIt;
<a name="l00176"></a>00176     <a class="code" href="classreranker_1_1_candidate_set_iterator.html" title="An interface specifying iteration over CandidateSet instances, using Java-style semantics (sorry...">CandidateSetIterator</a> *devtest_it =
<a name="l00177"></a>00177         <span class="keyword">new</span> CandidateSetVectorIt(devtest_examples);
<a name="l00178"></a>00178     model-&gt;NewEpoch(); <span class="comment">// sets epoch to 0</span>
<a name="l00179"></a>00179     model-&gt;Evaluate(*devtest_it);
<a name="l00180"></a>00180     loss = model-&gt;loss_per_epoch().back();
<a name="l00181"></a>00181     <span class="keyword">delete</span> devtest_it;
<a name="l00182"></a>00182   }
<a name="l00183"></a>00183   cout &lt;&lt; loss &lt;&lt; endl;
<a name="l00184"></a>00184 
<a name="l00185"></a>00185   <span class="keywordflow">return</span> 0;
<a name="l00186"></a>00186 }
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